intelect ppt arpanpal_security
TRANSCRIPT
Privacy Issues in Smart Living
Dr. Arpan Pal
Principal Scientist and Research Head
Innovation Lab, Kolkata
Tata Consultancy Services Ltd.
IEEE Sr. Member
Associate Editor, IEEE and ACM Transactions
B.Tech, M.Tech and Ph.D. in Electronics and Telecommunication
January 24, 2014
The Holy Grail of Privacy
Data that is both contextually useful as well as forever privacy preserving
• Privacy agreements are ok for legalities sake – but does the average user understand it?
https://www.privacyrights.org/fs/fs2b-cellprivacy.htm
• Main issue – Is the data I am giving out is worth the Utility I am getting?
PrivacyUtility
Smart Energy Meters – Utility
Accurate billing
Tailored energy efficiency advice – based on accurate data specific to your home
Understand how much appliances are costing you and check if things are working properly
More control over how much energy you’re using
Efficient peak-load management and Demand-response
http://www.efoodsdirect.com
Source: www.winlab.rutgers.edu/~gruteser/papers/fp023-roufPS.pdf
Smart Energy Meters – Privacy Issues
Could indicate your pattern of living and what you are doing in your own home
Bad guy knows when you're not at home and burgles your house, or worse, he knows when only one old woman is at home and breaks in
User Profiling
Occupancy / Footfall Detection – Utility
Energy consumption and carbon footprint can be reduced by using energy judiciously
• Occupancy and footfall give important feedback parameters for energy management in public places
• When person is at Home, room-level occupancy can be used to reduce energy footprint via automatic on-off switches
• At Office, zone/area level occupancy and presence can provide cues to energy management like lighting, heating and cooling
• In hospitals, zone level occupancy can provide input for running cooling / heating equipment.
http://www.lumenergize.com/http://pixgood.com/http://liveinnovations.com.auhttp://www.commlab.unimo.it
Occupancy Detection – Privacy Issues
Home occupancy data can reveal pattern of living / activity / absence at Home
Location data at Malls can reveal shopping behavior pattern
Recent MIT study showed that 4 spatio-temporal points, approximate places and times, are enough to uniquely identify 95% of 1.5M people in a mobility database
De Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel (March 25, 2013). "Unique in the Crowd: The privacy bounds of human mobility". Nature srep. Palmer, Jason (March 25, 2013). "Mobile location data 'present anonymity risk'". BBC News
http://www.etihad.com
Even Sleeping Smartphones Could Soon Hear Spoken CommandsNuance is working with chipmakers on technology that would enable “persistent listening” apps. http://www.technologyreview.com/news/429316/even-sleeping-smartphones-could-soon-hear-spoken-commands/
MIT Technology Review, Sept. 2012
Smartphone Malware Designed to Steal Your LifeThe US Naval Surface Warfare Center has created an Android app that secretly records your environment and reconstructs it as a 3D virtual model http://www.technologyreview.com/view/429394/placeraider-the-military-smartphone-malware-designed-to-steal-your-life/
MIT Technology Review, Sept. 2012
Vehicle Trip Overlay Over a Year reveals your hub locations (home, office??)Source: https://www.aclu.org/technology-and-liberty/meet-jack-or-what-government-could-do-all-location-data
Privacy Breach in other IoT Applications
Source: http://techcrunch.com/2014/11/13/u-s-authorities-are-reportedly-gathering-phone-data-using-fake-celltowers-on-planes/
What happens if this gathered data is leaked?
Big Brother Watching
Information-centric Approach Need something more than
anonymization and on-off control Hybrid approach using K-
Anonymity and Differential Privacy – selective masking / obfuscation of data
Information Theoretic Smart Privacy Analyzer (SPA) Statistical Processing based
Outlier Detection to identify Sensitive data
Information-theoretic privacy measure
Adaptation for differential privacy possible using variable sampling rate or obfuscation or randomization
Arijit Ukil et. al., BuildSys 2014 Demo
Points to Ponder• How to quantify utility and privacy• Privacy control at user hand
Thank [email protected]